ViTAD: Timing Violation-Aware RTL Debugging

• ViTAD is a framework that automates RTL timing-violation debugging by integrating static timing analysis, graph-based root-cause extraction, and LLM-guided repair.
• The system constructs a Signal Timing Dependency Graph from Verilog and timing reports, enabling precise violation-path extraction and targeted repair generation.
• Empirical results on 54 cases show a 73.68% fix rate, significantly outperforming LLM-only approaches and demonstrating its practical impact in VLSI design.

ViTAD is a framework for automating timing-violation debugging at the register-transfer level (RTL) in VLSI design. It combines static timing analysis, graph-based root-cause extraction, a domain-specific repair-strategy knowledge base, and LLMs to analyze violating timing paths and generate targeted RTL repairs. The system parses Verilog and timing reports to construct a Signal Timing Dependency Graph (STDG), extracts representative violation paths, infers likely causes of setup, hold, or CDC problems, and produces corrected Verilog code through retrieval-guided code generation (Lv et al., 18 Aug 2025). In the reported evaluation on 54 timing-violation cases derived from open-source RTL projects, ViTAD achieves a 73.68% fix rate, compared with 54.38% for an LLM-only baseline (Lv et al., 18 Aug 2025).

1. Role in RTL timing debugging

ViTAD is situated at the RTL stage of the VLSI design flow, where timing optimization remains especially consequential because early RTL changes propagate to later physical implementation steps at very high cost. The motivating premise is that, as device geometries shrink and clock speeds rise, even minor setup or hold violations can cause functional failures or system crashes. Conventional flows rely on static timing analysis (STA) tools after place-and-route to identify violations, but root-cause analysis and repair still depend heavily on engineers iterating manually over timing reports and code (Lv et al., 18 Aug 2025).

Within that context, ViTAD targets the longstanding asymmetry between mature timing-report generation and relatively manual timing-debug practice. Its stated objective is not merely to identify violating endpoints, but to automate the sequence of root-cause analysis, repair-strategy selection, and RTL patch generation. This suggests a shift from report-centric debugging toward a knowledge-driven synthesis pipeline in which structural circuit information and domain repair heuristics are explicitly provided to the LLM rather than left implicit in raw source code or free-form prompts.

2. Signal Timing Dependency Graph

The central static representation in ViTAD is the Signal Timing Dependency Graph, defined as

$\mathrm{STDG} = (V, E, w),$

where $V$ is the set of signals or registers in the RTL, $E=\{(u\to v)\mid u\text{ drives }v\}$ captures driving relations, and $w:E\to\mathbb{R}$ assigns an estimated propagation delay to each edge (Lv et al., 18 Aug 2025). ViTAD constructs this graph by parsing Verilog into an abstract syntax tree using Pyverilog to extract control-flow and data-flow relations, while simultaneously ingesting STA timing reports, such as those from Vivado, that identify violating endpoint registers and path delays.

Registers involved in a violation are annotated with their reported arrival time $T_{\text{arrival}}(r)$ and required time $T_{\text{required}}(r)$. Arrival and required times are then propagated over the graph using classical forward and backward recurrences:

$$T_{\text{arrival}}(v)=\max_{(u\to v)\in E}\bigl[T_{\text{arrival}}(u)+w(u\to v)\bigr],$$

$$T_{\text{required}}(u)=\min_{(u\to v)\in E}\bigl[T_{\text{required}}(v)-w(u\to v)\bigr].$$

Slack on an edge $e=(u\to v)$ is computed as

$$\mathrm{slack}(e)=T_{\text{required}}(v)-\bigl(T_{\text{arrival}}(u)+w(e)\bigr).$$

The STDG serves two functions. Factually, it merges logical signal dependency extracted from RTL with quantitative timing information extracted from STA. More broadly, it provides a compact intermediate representation in which timing failure is no longer a purely textual property of an STA report but a graph-localized phenomenon that can be traversed, ranked, and verbalized for downstream reasoning.

3. Violation-path extraction and root-cause localization

Given a violating register node $V$0, ViTAD extracts the set of simple directed paths in the STDG that terminate at that node. If $V$1 denotes all such paths, the system defines a violation path through a merge-or-selection operator $V$2:

$V$3

Operationally, the procedure performs a backward depth-first search from the violating node, collects source-to-endpoint paths whose cumulative delay exceeds the required time, and then selects a representative path $V$4 that minimizes slack:

$V$5

The extracted path is then summarized through structural and timing features. The paper lists path length, maximum individual edge delay, minimum slack, and the types of elements on the path, such as a deep mux or a long combinational chain, as key features used for diagnosis (Lv et al., 18 Aug 2025). This step is important because it narrows the debugging context before LLM inference. A plausible implication is that the method reduces distractor code context and converts a full-module debugging problem into a smaller causal explanation problem.

4. LLM-based diagnosis and knowledge-guided repair

After path extraction, ViTAD encodes the violation report into an LLM prompt. The example prompt format includes the endpoint register, arrival and required times, a symbolic path such as $V$6, per-stage delays, and slack, then asks the model to choose a likely root cause from candidates such as long_comb_chain, deep_mux, or low_pipe_stage and explain the diagnosis (Lv et al., 18 Aug 2025). The paper cites LLMs such as GPT-4o as possible inference engines.

ViTAD does not stop at diagnosis. It maintains a domain-specific knowledge base whose entries are tuples of the form (Violation Type, Subscenario, Repair Strategy, Example Patch). Retrieval is performed by embedding both the inferred subscenario text and knowledge-base entries with an embedding function $V$7 and ranking them by cosine similarity:

$V$8

The top-$V$9 retrieved entries guide repair generation. Typical repair strategies listed in the paper include inserting pipeline registers to break up long combinational chains, resizing or gating multiplexers, adding balanced buffer trees, and inserting synchronizer flop pairs for CDC errors (Lv et al., 18 Aug 2025). The selected strategy is injected into a parameterized prompt together with the original code fragment and example Verilog patch snippets, after which the LLM is asked to apply the transformation and produce corrected Verilog code.

This architecture is notable because the LLM is used in two distinct roles: first as a diagnosis engine over structured timing-path descriptions, and then as a code generator constrained by retrieved, scenario-specific repair knowledge. The paper’s ablation results indicate that both roles are materially important rather than interchangeable with generic prompting.

5. Dataset, metrics, and empirical results

The evaluation uses a timing-violation dataset of 54 cases constructed from open-source RTL modules, including PicoRV32 and OpenRISC, with injected realistic setup, hold, and CDC errors. Cases are labeled as “simple” or “complex” according to path depth and control-logic intricacy (Lv et al., 18 Aug 2025). Repair quality is measured by the Fix Rate (FR),

$E=\{(u\to v)\mid u\text{ drives }v\}$0

where a repair is successful if post-repair timing analysis reports no violations.

The reported results are as follows:

Method	Fix Rate	Condition
LLM-only	54.38%	no STDG or KB
LLM + domain knowledge text	60–62%	depending on the model
ViTAD	73.68%	full framework

ViTAD therefore improves the success rate by 19.30% over the LLM-only baseline, and the gain is reported as statistically significant with $E=\{(u\to v)\mid u\text{ drives }v\}$1 under a paired bootstrap test over the 54 cases (Lv et al., 18 Aug 2025). The paper further states that the method shows particularly large gains on complex deep_mux and long_comb_chain cases, where structured STDG analysis and tailored knowledge-base strategies guide the LLM more effectively than generic prompts. Ablation studies report that removing either explicit violation-path descriptions or retrieved repair-strategy guidance reduces FR by 10–20%.

These results support a narrow but important claim: within the evaluated setting, timing-aware structural abstraction and domain-specific retrieval improve automated RTL debugging beyond what is obtained by prompting an LLM directly on code and report text. This does not establish universal superiority for all timing-debug settings, but it does substantiate the framework’s utility on the dataset described in the paper.

6. Limitations, future directions, and nomenclature

The paper identifies several limitations. ViTAD can fail when the LLM misinterprets large code contexts or when the root cause is ambiguous, which may lead to incorrect patches. The proposed future directions include iterative backtracking loops described as “think-execute-feedback,” expanded knowledge-base coverage, and tighter integration with formal timing engines so that candidate repairs can be verified before code generation (Lv et al., 18 Aug 2025).

A common source of confusion is nomenclature. The acronym “ViTAD” is also used by an unrelated paper, “Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection” (Zhang et al., 2023). In the VLSI context, however, ViTAD refers specifically to “ViTAD: Timing Violation-Aware Debugging of RTL Code using LLMs” (Lv et al., 18 Aug 2025). The two works address different domains, problem formulations, and technical stacks, and should not be conflated.

Taken together, the reported design and results position ViTAD as a graph-augmented, knowledge-guided workflow for RTL timing debugging. Its main technical contribution lies in coupling classical timing abstractions with LLM inference and repair generation, rather than treating timing closure as an unconstrained code-editing task.